In this work, we present iShape, a new dataset designed for irregular Shape instance segmentation. Our dataset consists of six sub-datasets, namely iShape-Antenna, iShape-Branch, iShape-Fence, iShape-Log, iShape-Hanger, and iShape-Wire. As shown in Figure REF , each sub-dataset represents scenes of a typical irregular shape, for example, strip shape, hollow shape, and mesh shape. iShape has many characteristics that reflect the difficulty of instance segmentation for irregularly shaped objects. The most prominent one is the large overlaps between bounding boxes of objects, which is hard for proposal-based methods[1]}, [2]} due to feature ambiguity and non-maximum suppression (NMS [3]}). Meanwhile, overlapped objects that share the same center point challenge center-based methods[4]}, [5]}, [6]}. Another characteristic of iShape is a large number of objects with similar appearances, which makes embedding-based methods[7]}, [8]} hard to learn discriminative embedding. Besides, each sub-dataset has some unique characteristics. For example, iShape-Fence has about 53 connected components per instance, and iShape-Log has a large object scale variation due to various camera locations and perspective transformations. We hope that iShape can serve as a complement of existing datasets to promote the study of instance segmentation for irregular shape as well as arbitrary shape objects.
